CN105279492A - Iris identification method and device - Google Patents
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- CN105279492A CN105279492A CN201510695600.9A CN201510695600A CN105279492A CN 105279492 A CN105279492 A CN 105279492A CN 201510695600 A CN201510695600 A CN 201510695600A CN 105279492 A CN105279492 A CN 105279492A
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- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000001514 detection method Methods 0.000 claims abstract description 10
- 230000033228 biological regulation Effects 0.000 claims description 19
- 210000000720 eyelash Anatomy 0.000 claims description 16
- 238000010606 normalization Methods 0.000 claims description 12
- 210000000744 eyelid Anatomy 0.000 claims description 11
- 238000010586 diagram Methods 0.000 description 5
- 238000013519 translation Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 210000003786 sclera Anatomy 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
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- 230000015572 biosynthetic process Effects 0.000 description 1
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- 230000000694 effects Effects 0.000 description 1
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- 238000012417 linear regression Methods 0.000 description 1
- 210000001747 pupil Anatomy 0.000 description 1
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/18—Eye characteristics, e.g. of the iris
- G06V40/19—Sensors therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/18—Eye characteristics, e.g. of the iris
- G06V40/193—Preprocessing; Feature extraction
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Abstract
The invention discloses an iris identification method and device, and belongs to the field of iris identification. The iris identification method comprises the following steps: carrying out location and noise detection on an iris image to be identified so as to obtain an iris effective region; finding out a coincident part of the iris effective region of the iris image to be identified and a pre-stored iris effective region of an iris template so as to obtain a comparison effective area; determining a comparison threshold according to a predetermined corresponding relationship between the comparison effective area and the comparison threshold; and carrying out iris identification through the determined comparison threshold. Compared with the prior art, the iris identification method provided by the invention is capable of carrying out iris identification by using low-quality iris and has low false identification rate.
Description
Technical field
The present invention relates to iris recognition field, refer to a kind of method and apparatus of iris recognition especially.
Background technology
Iris recognition is as the safest in bio-identification, and the most accurate recognition methods more and more receives the concern of people.Iris is an annular viewing area between pupil and sclera, is be made up of the bacillar structure of complexity, has abundant, complicated texture structure.And the texture structure of these complexity constitutes the key character of iris recognition.The formation of iris is at embryonic stage stochastic generation, and everyone iris structure is different, and iris structure the changing hardly in life people of this uniqueness.Iris recognition mainly comprises the acquisition of iris image, and iris image quality is assessed, the pre-service of iris image, iris image normalization, the feature extraction of iris image and iris feature comparison.
Due in the process of actual iris identification, eyelash blocks, different people eyes size and eyes open the size that the degree of closing have impact on the useful area of iris, when the useful area of iris is too small (inferior quality iris), can there is a lot of problem in iris recognition, current solution mainly contains following two kinds of situations:
1). give up this iris image when useful area is less than setting value, will not register or identify, like this can be very poor for effect in the experience of the crowd of some pigsneys, even cannot use iris recognition;
2). reduce comparison threshold value, the less people's comparison of iris useful area can be allowed like this to pass through, but under same threshold value, easily cause and know by mistake.
Summary of the invention
The invention provides a kind of method and apparatus of iris recognition, the method can use inferior quality iris to carry out iris recognition, and misclassification rate is low.
For solving the problems of the technologies described above, the invention provides technical scheme as follows:
A method for iris recognition, comprising:
Iris image to be identified is positioned and walkaway, obtains iris effective coverage;
The part that the iris effective coverage of the iris effective coverage and the iris templates prestored of finding out iris image to be identified overlaps, obtains comparison useful area;
According to the corresponding relation of predetermined comparison useful area and comparison threshold value, determine comparison threshold value;
Iris recognition is carried out by the comparison threshold value determined.
A device for iris recognition, comprising:
Location and detection module, for positioning and walkaway iris image to be identified, obtain iris effective coverage;
Comparison useful area acquisition module, the part that the iris effective coverage for the iris effective coverage with the iris templates prestored of finding out iris image to be identified overlaps, obtains comparison useful area;
Comparison threshold determination module, for the corresponding relation according to predetermined comparison useful area and comparison threshold value, determines comparison threshold value;
Iris recognition module, carries out iris recognition for the comparison threshold value by determining.
The present invention has following beneficial effect:
First the present invention positions and walkaway iris image, obtains iris effective coverage and generted noise template; The registration of the iris effective coverage then determining iris image to be identified and the iris effective coverage of iris templates prestored, i.e. comparison useful area, and according to the corresponding relation of predetermined comparison useful area and comparison threshold value, determine comparison threshold value; Iris recognition is carried out finally by the comparison threshold value determined.The present invention is particularly useful for the identification (certainly, being also suitable for normal iris) of inferior quality iris.
Compared with prior art, the present invention uses different comparison threshold values to different comparison useful area, make low-quality iris image also can carry out registering and identifying, and misclassification rate is low.
Therefore the method for iris recognition of the present invention can use inferior quality iris to carry out iris recognition, and misclassification rate is low.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of a method embodiment of iris recognition of the present invention;
Fig. 2 is the schematic diagram of a device embodiment of iris recognition of the present invention;
Fig. 3 is to the schematic diagram that iris image positions in the present invention;
Fig. 4 is the schematic diagram of the noise template in the present invention;
Fig. 5 is the principle schematic that in the present invention, normalization launches;
Fig. 6 is the schematic diagram in the present invention after noise template normalization expansion;
Fig. 7 be sub-range in the present invention [0.4,0.5) an exemplary plot of ROC curve;
Fig. 8 be sub-range in the present invention [0.4,0.5) another exemplary plot of ROC curve;
Fig. 9 be sub-range in the present invention [0.4,0.5) the Still another example figure of ROC curve.
Embodiment
For making the technical problem to be solved in the present invention, technical scheme and advantage clearly, be described in detail below in conjunction with the accompanying drawings and the specific embodiments.
On the one hand, the invention provides a kind of method of iris recognition, one of them embodiment as shown in Figure 1, comprising:
Step S101: position and walkaway iris image to be identified, obtains iris effective coverage; Iris effective coverage refers to the region that can obtain iris texture characteristic, and concrete is generally removes in the iris region of finger ring shape that eyelid blocks, eyelashes block and region after noise.
Step S102: the part that the iris effective coverage of the iris effective coverage and the iris templates prestored of finding out iris image to be identified overlaps, obtains comparison useful area; Comparison useful area can reflect the information registration of iris image to be identified and iris templates, the comparison threshold value adopted when can be used for judging whether for carrying out identifying and identifying.
Step S103: according to the corresponding relation of predetermined comparison useful area and comparison threshold value, determine comparison threshold value; Comparison useful area can be used for determining comparison threshold value, concrete, when comparison useful area is larger, comparison threshold value suitably can be improved, when comparison useful area is less, comparison threshold value suitably can be reduced, the present embodiment obtains the corresponding relation of comparison useful area and comparison threshold value in advance, according to comparison useful area, can determine comparison threshold value, this comparison threshold value makes misclassification rate low.The corresponding relation of comparison useful area and comparison threshold value can be the curve that prior matching is good, also can be to the corresponding form etc. of useful area with comparison threshold value.
Step S104: carry out iris recognition by the comparison threshold value determined; After obtaining comparison threshold value, just can carry out iris recognition according to existing various method.
First the present embodiment positions and walkaway iris image, obtains iris effective coverage and generted noise template; The registration of the iris effective coverage then determining iris image to be identified and the iris effective coverage of iris templates prestored, i.e. comparison useful area, and according to the corresponding relation of predetermined comparison useful area and comparison threshold value, determine comparison threshold value; Iris recognition is carried out finally by the comparison threshold value determined.The present embodiment is particularly useful for the identification (certainly, being also suitable for normal iris) of inferior quality iris.
Compared with prior art, the present embodiment uses different comparison threshold values to different comparison useful area, make low-quality iris image also can carry out registering and identifying, and misclassification rate is low.
Therefore the method for the iris recognition of the present embodiment can use inferior quality iris to carry out iris recognition, and misclassification rate is low.
One as the method for iris recognition of the present invention is improved, the part that the iris effective coverage of the iris effective coverage and the iris templates prestored of finding out iris image to be identified overlaps, and obtains comparison useful area (step S102) and comprising:
Step S1021: according to locating the iris effective coverage obtained, generted noise template; Noise template refers to and is represented by different values respectively in iris effective coverage and other regions, distinguishes.
Step S1022: the noise template obtained and the noise template prestored are sought common ground, obtains comparison useful area; Comparison useful area is now the part that the iris effective coverage of the noise template obtained and the noise template prestored coincides.
The present embodiment is sought common ground by 2 noise templates and determines comparison useful area, and method complexity is low, simple and convenient, and travelling speed is fast.
Another kind as the method for iris recognition of the present invention improves, and positions and walkaway iris image to be identified, obtains iris effective coverage (step S101) and comprising:
Step S1011: location pupil-iris boundary, iris-sclera border, border, upper eyelid and palpebra inferior border; As shown in Figure 3.
Step S1012: detect eyelash and hot spot; Iris effective coverage is pupil-iris boundary, iris-sclera border, remove the region of eyelash and hot spot between border, upper eyelid and palpebra inferior border.
Location mainly comprises iris-pupil boundary location, iris-sclera boundary alignment, upper eyelid boundary alignment and palpebra inferior boundary alignment, and walkaway mainly comprises eyelash and spot detection.Main method has Sobel operator Boundary Detection, infinitesimal analysis detective operators, Hough transform, least square curve fit etc.
The present embodiment determines iris effective coverage by boundary alignment and eyelash and spot detection, simple and convenient, avoids the impact of the noise such as eyelash and hot spot.
Further, noise template can obtain by the following method:
The pixel value of the pixel of iris effective coverage is set to 255, and the pixel value of the pixel in all the other regions is set to 0.Certainly, be a preferred embodiment here, 2 different arbitrarily numerical value in fact can be used to replace 255 and 0 respectively, only calculate more convenient like this, and be convenient to distinguish.
Generted noise template on the basis of effective iris region, concrete grammar is that the pixel value of the pixel of the eyelash region detected and spot area is all set to 0, the pixel value of the pixel in the region of the pixel of iris-pupil boundary line inside is set to 0, the pixel value of the pixel of iris-perimeter, sclera boundary line is set to 0, the pixel value of the pixel in region more than boundary line, upper eyelid is set to 0, palpebra inferior boundary line is set to 0 with the pixel value of the pixel of lower area, in iris image, the pixel value of the pixel of remainder is set to 255, the noise template of final generation as shown in Figure 4.
Iris effective coverage and the pixel value of the pixel in other regions are set to 255 and 0 by the present embodiment respectively, and margin of image element, apart from large, be convenient to distinguish, and facilitated follow-up calculating.
As another improvement of the method for iris recognition of the present invention, according to locating the iris effective coverage obtained, after generted noise template (step S1021), the noise template obtained and the noise template prestored are sought common ground, also comprise before obtaining comparison useful area (step S1022):
Noise template normalization between pupil-iris boundary and iris-sclera border is launched.
Because the pixel value of the pixel of the part outside the part within pupil-iris boundary in noise template and iris-sclera border is 0, and by the information less than these parts in follow-up iris recognition, therefore we only need the noise template between pupil-iris boundary and iris-sclera border to carry out operating, and reduce operand; And consider that iris exists translation, rotate, the problems such as dimensional variation, need the region noise template normalization between the pupil-iris boundary of annular and iris-sclera border being expanded into rectangle, eliminate the inconsistency of iris image size, and facilitate follow-up calculating, concrete principle as shown in Figure 5, wherein, r ∈ [0,1], θ ∈ [0,2 π], the marginal point of the inside and outside circle of the iris on θ direction can use (x
in, y
in), (x
out, y
out) represent.As shown in Figure 6, the accounting that wherein white portion (pixel value size is 255) accounts for whole region is the area of iris effective coverage to design sketch after expansion, and in order to better expression, unfolded image has added the grey marginal point of certain length.Which reduce operand, facilitate follow-up calculating, and eliminate iris image translation, rotate, the inconsistency of the size that dimensional variation is brought.
And, the noise template obtained and the noise template prestored are sought common ground, obtain comparison useful area (step S1022) and comprising:
The pixel value of the noise template obtained with each pixel of the noise template prestored is carried out and computing;
Calculate with computing after be the ratio that the pixel number of true (being 255) accounts for the total number of noise template pixel, obtain comparison useful area.
For two iris images (iris image of iris image to be identified and registered in advance) to be compared, the noise template (the noise template of iris image to be identified and the noise template of registered in advance) generated is carried out intersection operation, suppose that two noise templates are Noise1 and Noise2, getting the noise template after common factor is Noise, and concrete operation method is as follows:
By pixel value be the pixel number of 255 divided by the total number of noise template pixel, be comparison useful area.
The present embodiment launches to decrease operand to the noise template normalization between pupil-iris boundary and iris-sclera border, facilitates follow-up calculating, and eliminates iris image translation, rotates, the inconsistency of the size that dimensional variation is brought; Using with computing after account for the ratio of the total number of noise template pixel as comparison useful area for genuine pixel number, make the value of comparison useful area for [0,1], quantize and convenience of calculation, and the present invention uses intersection operation to try to achieve comparison useful area, simple and convenient.
As another improvement of the method for iris recognition of the present invention, the present invention is topmost to classify to iris image to be identified according to comparison useful area exactly, collects sample and under different comparison useful area, draws comparison threshold value-misclassification rate curve respectively.By the FAR (misclassification rate) under the comparison useful area that comparison threshold value-misclassification rate curve statistical is different, and the score value of correspondence.Concrete steps are as follows:
The comparison useful area of the multiple samples in iris sample set is divided into several sub-ranges, namely according to the size of comparison useful area, these samples is classified; Concrete sub-range criteria for classification can be divided into 5 sub-ranges, be respectively [0.2,0.3), [0.3,0.4), [0.4,0.5), [0.5,0.6), [0.6,1], to each sub-range, performs following operation respectively:
Sample corresponding to each sub-range under different comparison threshold values carries out iris recognition, adds up the misclassification rate under different comparison threshold value; 10000 positive samples (iris of same person) can be added up in each sub-range, 200000 negative samples (iris of different people).
With comparison threshold value for horizontal ordinate, misclassification rate is ordinate, draws comparison threshold value-misclassification rate curve;
Under the misclassification rate of regulation, with the mid point in each sub-range for horizontal ordinate, comparison threshold value is ordinate, obtains the coordinate corresponding relation between sub-range under regulation misclassification rate and threshold value;
With sub-range [0.4, 0.5) be example, drafting comparison threshold value-misclassification rate curve is carried out by 10000 positive samples in this sub-range and 200000 negative samples, comparison threshold value-misclassification rate curve obtains the comparison threshold value under regulation misclassification rate, regulation misclassification rate can have multiple herein, we select FAR (misclassification rate) to be ten thousand/, 100000/and zero is the misclassification rate specified, obtain corresponding comparison threshold value, then with sub-range [0.4, 0.5) mid point 0.45 is horizontal ordinate, comparison threshold value is ordinate, obtain three coordinate points (being the coordinate corresponding relation between sub-range and threshold value), respectively corresponding FAR be ten thousand/, 100000/and zero.Other sub-ranges process equally.
Fig. 7 to Fig. 9 gives sub-range [0.4, 0.5) comparison threshold value-misclassification rate curve, horizontal ordinate is comparison threshold value, ordinate is percent, a curve wherein on a declining curve is comparison threshold value-misclassification rate curve, in Fig. 7, when misclassification rate is 0.0001 (ten thousand/), comparison threshold value is 69, in Fig. 8, when misclassification rate is 0.00001 (100,000/), comparison threshold value is 77, in Fig. 9, when misclassification rate is 0, comparison threshold value is 83, then with sub-range [0.4, 0.5) mid point 0.45 is horizontal ordinate, comparison threshold value is ordinate, so just to obtain comparison threshold value be ten thousand/, 100000/and zero time three coordinate points (0.45, 69), (0.45, 77) and (0.45, 83).
In addition, in Fig. 7 to Fig. 9, a curve in rising trend is comparison threshold value-refuse sincere curve, sample of refusing really to make a comment or criticism is unrecognized by (corresponding, aforesaid knowledge by mistake refers to that negative sample is identified and have passed), comparison threshold value-refuse true curve can be determined under different occasion, use different misclassification rates to provide reference data for us, and avoid refusing the sincere too high user that should be identified by caused and repeatedly identify the problem do not passed through, user experience is good.In Fig. 7, when comparison threshold value is 69, refusing sincere is in 0.0142, Fig. 8, and when comparison threshold value is 77, refusing sincere is in 0.02139, Fig. 9, and when comparison threshold value is 83, refusing sincere is 0.03025.We are by comparison threshold value-misclassification rate curve and comparison threshold value-refuse true curve to be collectively referred to as ROC curve.
Sub-range according to the rules under misclassification rate and the coordinate corresponding relation between comparison threshold value, by the means such as least square method or method of interpolation, carry out curve fitting, and obtains the relation of comparison useful area and comparison threshold value under regulation misclassification rate.
For FAR be ten thousand/, obtain respectively [0.2,0.3), [0.3,0.4), [0.4,0.5), [and 0.5,0.6), the coordinate points in [0.6,1] these 5 sub-ranges, utilizes equation of linear regression y=a
0+ a
1x is that ten thousand/for the moment 5 coordinate points carry out matching to FAR, calculates a by least square method
0and a
1value, to obtain FAR be ten thousand/relation of comparison useful area and comparison threshold value for the moment.FAR is 100,000/with zero time computing method identical.
Sample in iris sample set of the present invention will ensure enough othernesses, namely needs the image collecting different eyes size, to guarantee the sample obtaining different comparison useful area.The comparison useful area of the multiple samples in iris sample set can obtain in advance, also can be to be obtained by method above.
The present invention is by obtaining comparison threshold value-misclassification rate curve to the test of iris sample set, by the comparison threshold value of comparison threshold value-misclassification rate curve different comparison useful area under determining regulation misclassification rate, then matching is carried out, obtain the corresponding relation of comparison useful area and comparison threshold value, comparison useful area is corresponding with comparison threshold value accurately.And the comparison useful area that can obtain under multiple misclassification rate and the corresponding relation of comparison threshold value, be applicable to different application scenarios.
On the other hand, the invention provides a kind of device of iris recognition, one of them embodiment as shown in Figure 2, comprising:
Location and detection module 11, for positioning and walkaway iris image to be identified, obtain iris effective coverage;
Comparison useful area acquisition module 12, the part that the iris effective coverage for the iris effective coverage with the iris templates prestored of finding out iris image to be identified overlaps, obtains comparison useful area;
Comparison threshold determination module 13, for the corresponding relation according to predetermined comparison useful area and comparison threshold value, determines comparison threshold value;
Iris recognition module 14, carries out iris recognition for the comparison threshold value by determining.
The device of the iris recognition of the present embodiment can use inferior quality iris to carry out iris recognition, and misclassification rate is low.
One as the device of iris recognition of the present invention is improved, and comparison useful area acquisition module comprises:
Noise template generation unit, for the iris effective coverage obtained according to location, generted noise template;
Comparison useful area acquiring unit, for the noise obtained template and the noise template prestored being sought common ground, obtains comparison useful area.
The present embodiment is sought common ground by 2 noise templates and determines comparison useful area, and method complexity is low, simple and convenient, and travelling speed is fast.
Another kind as the device of iris recognition of the present invention improves, and location and detection module comprise:
Positioning unit, for locating pupil-iris boundary, iris-sclera border, border, upper eyelid and palpebra inferior border;
Detecting unit, for detecting eyelash and hot spot; Iris effective coverage is pupil-iris boundary, iris-sclera border, remove the region of eyelash and hot spot between border, upper eyelid and palpebra inferior border.
The present embodiment determines iris effective coverage by boundary alignment and eyelash and spot detection, simple and convenient, avoids the impact of the noise such as eyelash and hot spot.
As another improvement of the device of iris recognition of the present invention, after noise template generation module, also comprise before comparison useful area acquisition module:
After noise template generation unit, also comprise before comparison useful area acquiring unit:
Normalization expanding unit, for launching the noise template normalization between pupil-iris boundary and iris-sclera border.
Comparison useful area acquiring unit comprises:
With arithmetic element, for the pixel value of the noise obtained template with each pixel of the noise template prestored is carried out and computing;
Ratio computing unit, for calculate with computing after account for the ratio of the total number of noise template pixel for genuine pixel number, obtain comparison useful area.
The present embodiment launches to decrease operand to the noise template normalization between pupil-iris boundary and iris-sclera border, facilitates follow-up calculating, and eliminates iris image translation, rotates, the inconsistency of the size that dimensional variation is brought; Using with computing after account for the ratio of the total number of noise template pixel as comparison useful area for genuine pixel number, make the value of comparison useful area for [0,1], quantize and convenience of calculation, and the present invention uses intersection operation to try to achieve comparison useful area, simple and convenient.
As another improvement of the device of iris recognition of the present invention, the corresponding relation of comparison useful area and comparison threshold value is by obtaining with lower module:
Sub-range module, for the comparison useful area of the multiple samples in iris sample set is divided into several sub-ranges, to each sub-range, performs following operation respectively:
Iris recognition unit, carries out iris recognition for sample corresponding to each sub-range under different comparison threshold values, adds up the misclassification rate under different comparison threshold value;
Drawing of Curve unit, for comparison threshold value for horizontal ordinate, misclassification rate is ordinate, draws comparison threshold value-misclassification rate curve;
Comparison threshold value acquiring unit, for according to described comparison threshold value-misclassification rate curve, obtains the comparison threshold value under regulation misclassification rate;
Coordinate determining unit, under the misclassification rate of regulation, with the mid point in each sub-range for horizontal ordinate, comparison threshold value is ordinate, obtains the coordinate corresponding relation between sub-range under regulation misclassification rate and threshold value;
Fitting module, for the coordinate corresponding relation between the sub-range under misclassification rate according to the rules and threshold value, carries out curve fitting, and obtains the relation of comparison useful area and comparison threshold value under regulation misclassification rate.
The present embodiment is by obtaining comparison threshold value-misclassification rate curve to the test of iris sample set, by the comparison threshold value of comparison threshold value-misclassification rate curve different comparison useful area under determining regulation misclassification rate, then matching is carried out, obtain the corresponding relation of comparison useful area and comparison threshold value, comparison useful area is corresponding with comparison threshold value accurately.And the corresponding relation of multiple comparison useful area and comparison threshold value can be obtained, be applicable to different application scenarios.
The above is the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the prerequisite not departing from principle of the present invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (10)
1. a method for iris recognition, is characterized in that, comprising:
Iris image to be identified is positioned and walkaway, obtains iris effective coverage;
The part that the iris effective coverage of the iris effective coverage and the iris templates prestored of finding out iris image to be identified overlaps, obtains comparison useful area;
According to the corresponding relation of predetermined comparison useful area and comparison threshold value, determine comparison threshold value;
Iris recognition is carried out by the comparison threshold value determined.
2. the method for iris recognition according to claim 1, is characterized in that, described in find out iris image to be identified the part that overlaps of the iris effective coverage of iris effective coverage and the iris templates prestored, obtain comparison useful area and comprise:
According to locating the iris effective coverage obtained, generted noise template;
The noise template obtained and the noise template prestored are sought common ground, obtains comparison useful area.
3. the method for iris recognition according to claim 2, is characterized in that, describedly positions and walkaway iris image to be identified, obtains iris effective coverage and comprises:
Location pupil-iris boundary, iris-sclera border, border, upper eyelid and palpebra inferior border;
Detect eyelash and hot spot; Described iris effective coverage is pupil-iris boundary, iris-sclera border, remove the region of eyelash and hot spot between border, upper eyelid and palpebra inferior border.
4. the method for iris recognition according to claim 3, is characterized in that:
Described according to locating the iris effective coverage that obtains, after generted noise template, described the noise template obtained and the noise template that prestores to be sought common ground, also comprise before obtaining comparison useful area:
Noise template normalization between described pupil-iris boundary and iris-sclera border is launched.
Described the noise template obtained and the noise template that prestores to be sought common ground, obtain comparison useful area and comprise:
The pixel value of the noise template obtained with each pixel of the noise template prestored is carried out and computing;
Be the ratio that genuine pixel number accounts for the total number of noise template pixel after calculating and computing, obtain comparison useful area.
5., according to the method for the arbitrary described iris recognition of claim 1-4, it is characterized in that, the corresponding relation of comparison useful area and comparison threshold value obtains by the following method:
The comparison useful area of the multiple samples in iris sample set is divided into several sub-ranges, to each sub-range, performs following operation respectively:
Sample corresponding to each sub-range under different comparison threshold values carries out iris recognition, adds up the misclassification rate under different comparison threshold value;
With comparison threshold value for horizontal ordinate, misclassification rate is ordinate, draws comparison threshold value-misclassification rate curve;
According to described comparison threshold value-misclassification rate curve, obtain the comparison threshold value under regulation misclassification rate;
Under the misclassification rate of regulation, with the mid point in each sub-range for horizontal ordinate, comparison threshold value is ordinate, obtains the coordinate corresponding relation between sub-range under regulation misclassification rate and threshold value;
Sub-range according to the rules under misclassification rate and the coordinate corresponding relation between threshold value, carry out curve fitting, and obtains the relation of comparison useful area and comparison threshold value under regulation misclassification rate.
6. a device for iris recognition, is characterized in that, comprising:
Location and detection module, for positioning and walkaway iris image to be identified, obtain iris effective coverage;
Comparison useful area acquisition module, the part that the iris effective coverage for the iris effective coverage with the iris templates prestored of finding out iris image to be identified overlaps, obtains comparison useful area;
Comparison threshold determination module, for the corresponding relation according to predetermined comparison useful area and comparison threshold value, determines comparison threshold value;
Iris recognition module, carries out iris recognition for the comparison threshold value by determining.
7. the device of iris recognition according to claim 6, is characterized in that, described comparison useful area acquisition module comprises:
Noise template generation unit, for the iris effective coverage obtained according to location, generted noise template;
Comparison useful area acquiring unit, for the noise obtained template and the noise template prestored being sought common ground, obtains comparison useful area.
8. the device of iris recognition according to claim 7, is characterized in that, described location and detection module comprise:
Positioning unit, for locating pupil-iris boundary, iris-sclera border, border, upper eyelid and palpebra inferior border;
Detecting unit, for detecting eyelash and hot spot; Described iris effective coverage is pupil-iris boundary, iris-sclera border, remove the region of eyelash and hot spot between border, upper eyelid and palpebra inferior border.
9. the device of iris recognition according to claim 8, is characterized in that:
After described noise template generation unit, also comprise before described comparison useful area acquiring unit:
Normalization expanding unit, for launching the noise template normalization between described pupil-iris boundary and iris-sclera border.
Described comparison useful area acquiring unit comprises:
With arithmetic element, for the pixel value of the noise obtained template with each pixel of the noise template prestored is carried out and computing;
Ratio computing unit, for calculate with computing after account for the ratio of the total number of noise template pixel for genuine pixel number, obtain comparison useful area.
10., according to the device of the arbitrary described iris recognition of claim 6-9, it is characterized in that, the corresponding relation of comparison useful area and comparison threshold value is by obtaining with lower module:
Sub-range module, for the comparison useful area of the multiple samples in iris sample set is divided into several sub-ranges, to each sub-range, performs following operation respectively:
Iris recognition unit, carries out iris recognition for sample corresponding to each sub-range under different comparison threshold values, adds up the misclassification rate under different comparison threshold value;
Drawing of Curve unit, for comparison threshold value for horizontal ordinate, misclassification rate is ordinate, draws comparison threshold value-misclassification rate curve;
Comparison threshold value acquiring unit, for according to described comparison threshold value-misclassification rate curve, obtains the comparison threshold value under regulation misclassification rate;
Coordinate determining unit, under the misclassification rate of regulation, with the mid point in each sub-range for horizontal ordinate, comparison threshold value is ordinate, obtains the coordinate corresponding relation between sub-range under regulation misclassification rate and threshold value;
Fitting module, for the coordinate corresponding relation between the sub-range under misclassification rate according to the rules and threshold value, carries out curve fitting, and obtains the relation of comparison useful area and comparison threshold value under regulation misclassification rate.
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